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Study urges caution when comparing neural networks to the brain

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Re: Study urges caution when comparing neural networks to the brain

#121

Post by nl »

retrac wrote: post #106
nl wrote: post #102

Earlier quoted context omitted.

> that there is speed-of-light quantum communication within the brain What does this even mean?

Einstein's spooky action at a distance, between neurons. This is speculative, maybe recklessly so, but one possible interpretation is that these are neurotransmitters. The halves of the entangled pair float off and they bind to different receptors, and do their usual neurotransmitter thing of affecting how the neuron fires. But they are entangled, so theoretically the quantum state of one half could affect the other…

"Spooky action at a distance" propagates at faster than light speed.

That's why Einstein thought it was spooky! But in the widespread interpretation[1] of quantum entanglement it turns out not to be a problem because (while entanglement effects are real) it's impossible to transmit information or action via it.

Worth noting that this link doesn't talk about that at all. Instead it's about quantum chemistry effects.

[1] https://en.wikipedia.org/wiki/Copenhagen_interpretation#Acce...

Re: Study urges caution when comparing neural networks to the brain

#122

Post by [deleted] »

19h wrote: post #120

We've been doing significant research using HTM[1][2][3][4] w/ SDR[5] similar to the proprietary implementation of Semantic Folding[6] and were able to classify literally gigabytes of documents per second on FPGAs. HTM aims to resemble the architecture of the Neocortex. I simply don't understand why literally everyone immediately jumps at CNNs, RNNs (transformers et al.) -- they're extremely expensive, slow and defin…

[deleted]

Re: Study urges caution when comparing neural networks to the brain

#123

Post by chaxor »

Teever wrote: post #31
lalos wrote: post #28

Here's my guess: neurons tap into quantum mechanics but we are too primitive to understand that for now. The brain was initially modeled as humors/fluids back when we developed aqueducts, then telegraph came into the scene and it was modeled as electrical impulses and now computers/ML are popular therefore we see it as a neural network. Next step is quantum.

But why is the next step quantum? And why is this the final step?

People don't understand quantum physics. People also don't understand AGI.

Therefore it's obvious that they're related. So it seems clear that AGI will be solved with the help of quantum physics.

My aunt Mildred is a very well renown academic and has written much on this topic. She unfortunately is also not well understood. So it seems quite clear - perhaps obvious - that AGI will be solved by applying some Mildred.

Re: Study urges caution when comparing neural networks to the brain

#124

Post by fjkdlsjflkds »

cuteboy19 wrote: post #118
colechristensen wrote: post #90

Earlier quoted context omitted.

This is often a big part of how neural networks are trained. Take labelled photos (really it applies in abstract ways to other data) add a little noise, mirror, shrink and stretch, translate to a different area, etc. One piece of data becomes several training images. And no, this standard practice does not eliminate adversaries.

No not while training, just ask the model to predict for a few transforms and get the mode. Simulating the fact that humans also have multiple frames worth of information about any object from slightly different angles

This is actually a thing, yes (and it does increase robustness to adversarial examples). This is particularly useful if you apply (random) low-pass filtering (or denoising, or DCT-based compression, or anything that messes up high-frequency content) to the image (besides random cropping/rescaling), since adversarial examples often rely on manipulating (human-imperceptible) high-frequency information.

Re: Study urges caution when comparing neural networks to the brain

#125

Post by maze-le »

dotnet00 wrote: post #96
constantcrying wrote: post #33

To me one important aspect is the existence of adversarially attacks on neural networks. They essentially prove that the neural network never "understood" its data. It hasn't found some general categories which correspond somewhat to human categories. Human brains can be tricked too, but never this way and never beyond our capacities for rational thought.

"Never this way and never beyond our capacities for rational thought" makes it so nothing can be stated about the "understanding" any neural network has about any data short of an AGI. Which is obviously not too useful because an incomplete model of something is not the same as not having the model at all. Eg. if the model is generating too many arms, that still means it has extracted some model of what an arm looks…

The main difference to optical illusions is that we are aware of them and integrate this knowledge into our model of the world, so that we can deal with them to a certain extend.

Re: Study urges caution when comparing neural networks to the brain

#126

Post by ben_w »

maze-le wrote: post #125
dotnet00 wrote: post #96

Earlier quoted context omitted.

"Never this way and never beyond our capacities for rational thought" makes it so nothing can be stated about the "understanding" any neural network has about any data short of an AGI. Which is obviously not too useful because an incomplete model of something is not the same as not having the model at all. Eg. if the model is generating too many arms, that still means it has extracted some model of what an arm looks…

The main difference to optical illusions is that we are aware of them and integrate this knowledge into our model of the world, so that we can deal with them to a certain extend.

We're not always aware of optical illusions.

Not even dynamic ones.

Magicians will tell you they're fooling you, but con artists can use many of the same patterns and you only find out too late.

Re: Study urges caution when comparing neural networks to the brain

#127

Post by elcomet »

ben_w wrote: post #76
elcomet wrote: post #74

Earlier quoted context omitted.

And you didn't even count the data from million of years of evolution. The brain doesn't come as a blank slate when you're born.

That's literally what I was saying when I wrote "our biases are how evolution managed to do that".

Yeah but I disagreed about your point "Current AI requires far more examples than we do to learn from", since I think you need to count the amount of data that was seen by all your ancesters, maybe even starting from the first self-replicating molecule, billion years ago.

Re: Study urges caution when comparing neural networks to the brain

#128

Post by Jensson »

ben_w wrote: post #126
maze-le wrote: post #125

Earlier quoted context omitted.

The main difference to optical illusions is that we are aware of them and integrate this knowledge into our model of the world, so that we can deal with them to a certain extend.

We're not always aware of optical illusions. Not even dynamic ones. Magicians will tell you they're fooling you, but con artists can use many of the same patterns and you only find out too late.

Con artists isn't a big problem, if it worked on everyone then you would have con artists become the richest persons in the world. Like, just con Elon Musk out of his billions, why hasn't anyone done that yet if it is so easy to trick humans?

Re: Study urges caution when comparing neural networks to the brain

#129

Post by [deleted] »

cameronh90 wrote: post #59
bawolff wrote: post #38

The aspect of ai that makes me think something related is going on, is how artifacts look in image generation systems like stable diffusion. Often these systems will have really bizzare artificats, people with 3 arms, etc. However at the same time when you glance at the output without looking carefully you will sometimes miss these artifacts even though they should be absolutely glaring.

Check out the Velocopedia project for something else along this line of thinking: http://www.gianlucagimini.it/prototypes/velocipedia.html Turns out nobody quite knows how to draw a bicycle. They get the gist but the details don't make sense.

[deleted]

Re: Study urges caution when comparing neural networks to the brain

#130

Post by nl »

19h wrote: post #120

We've been doing significant research using HTM[1][2][3][4] w/ SDR[5] similar to the proprietary implementation of Semantic Folding[6] and were able to classify literally gigabytes of documents per second on FPGAs. HTM aims to resemble the architecture of the Neocortex. I simply don't understand why literally everyone immediately jumps at CNNs, RNNs (transformers et al.) -- they're extremely expensive, slow and defin…

> I simply don't understand why literally everyone immediately jumps at CNNs, RNNs (transformers et al.) -- they're extremely expensive, slow

Because for text they work a lot better.

HTMs are competitive with other non-NN techniques on small datasets (see [1] you listed) but nothing particularly amazing.

I'd speculate this is because they use bag-of-word variants like LSI and TF-IDF. Prior to Transformers this was a competitive technique and you could get state-of-the-art results on most things using similar techniques.

This representation of data matters a lot. Even just switching to a word embedding representation and using a SVM or something gives a decent gain in most circumstances.

But transformers are much better, particularly on harder tasks (eg question answering on long documents). You can't really see how significant this difference is on these small datasets, but as an example BertGCN is getting over 89% accuracy (HTM in [1] gets 83%).

It's possible (likely!) some of this gain is from the better representation Transformers use, not just the model.

> definitely not usable for SIGINT-sized intel projects

If SIGINT in this context means signal intelligence (on text data) then I assure you that they are being used.

[1] https://arxiv.org/pdf/2112.14820.pdf

[2] https://paperswithcode.com/paper/bertgcn-transductive-text-c...

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